Question

文本分类器（基于KNN算法），语言最好是Matlab的，有测试数据集。。。。

Answer 1

function [ccr,pgroupt]=knnt(x,group,K,dist,xt,groupt)
%#
%# AIM: to classify test set objects or unknown objects with the
%# K Nearest Neighbour method
%#
%# PRINCIPLE: KNN is a supervised, deterministic, non-parametric
%# classification method. It uses the majority rule to
%# assign new objects to a class.
%# It is assumed that the number of objects in each class
%# is similar.
%# There are no assumptions about the data distribution and
%# the variance-covariance matrices of each class.
%# There is no limitation of the number of variables when
%# the Euclidean distance is used.
%# However, when the correlation coefficient is used, the
%# number of variables must be larger than 1.
%# Ref: Massart D. L., Vandeginste B. G. M., Deming S. N.,
%# Michotte Y. and Kaufman L., Chemometrics: a textbook,
%# Chapter 23, 395-397, Elsevier Science Publishers B. V.,
%# Amsterdam 1988.
%#
%# INPUT: x: (mxn) data matrix with m objects and n variables,
%# containing samples of several classes (training set)
%# group: (mx1) column vector labelling the m objects from the
%# training set
%# K: integer, number of nearest neighbours
%# dist: integer,
%# = 1, Euclidean distance
%# = 2, Correlation coefficient, (No. of variables >1)
%# xt: (mtxn) data matrix with mt objects and n variables
%# (test set or unknowns)
%# groupt: (mtx1) column vector labelling the mt objects from
%# the test set
%# --> if the new objects are unknown, input [].
%#
%# OUTPUT: ccr: scalar, correct classification rate
%# pgroupt:row vector, predicted class label for the test set
%# 0 means that the object is not classified to any
%# class
%#
%# SUBROUTINES: sortlab.m: sorts the group label vector into classes
%#
%# AUTHOR: Wen Wu
%# Copyright(c) 1997 for ChemoAc
%# FABI, Vrije Universiteit Brussel
%# Laarbeeklaan 103 1090 Jette
%#
%# VERSION: 1.1 (28/02/1998)
%#
%# TEST: Andrea Candolfi
%#

function [ccr,pgroupt]=knnt(x,group,K,dist,xt,groupt);

if nargin==5, groupt=[]; end % for unknown objects
distance=dist; clear dist % change variable
if size(group,1)>1,
group=group'; % change column vector into row vector
groupt=groupt'; % change column vector into row vector
end;
[m,n]=size(x); % size of the training set

if distance==2 & n<2, error('Number of variables must > 1'),end % to check the number of variables when using correlation coefficient

[mt,n]=size(xt); % size of the test set
dis=zeros(mt,m); % initial values for the distance (matrix of zeros)

% Calculation of the distance for each test set object
for i=1:mt
for j=1:m % between each training set object and each test set object
if distance==1
dis(i,j)=(xt(i,:)-x(j,:))*(xt(i,:)-x(j,:))'; % Euclidian distance
else
r=corrcoef(xt(i,:)',x(j,:)'); % Correlation coefficient matrix
r=r(1,2); % Correlation coefficient
dis(i,j)=1-r*r; % 1 - the power of correlation coefficient
end
end
end

% Finding of the nearest neighbours
lab=zeros(1,mt); % initial values of lab
for i=1:mt % for each test object
[a,b]=sort(dis(i,:)); % sort distances
b=b(find(a<=a(K))); % to find the nearest neighbours indices
b=group(b); % the nearest neighbours objects
[ng,lgroup]=sortlab(b); % calculate the number of objects from each class in the nearest neighbours
a=find(ng==max(ng)); % find the class with the maximum number of objects

if length(a)==1 % only one class
lab(i)=lgroup(a); % class label
else
lab(i)=0; % more than one class
end
end

% Calculation of the success rate
if ~isempty(groupt)
dif=groupt-lab; % difference between predicted class label and known class label
ccr=sum(dif==0)/mt; % success rate
end

pgroupt=lab; % the output vector

Answer 2

好像这些算法在数据缺失的情况下是没法进行的吧，只能说改进之后在数据缺失情况下做了相应处理，你说的这些算法都可以在网上找到代码

Answer 3

ME5